SANM

Source code for our CVPR paper Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Learning with Noisy labels via Self-supervised Adversarial Noisy Masking (CVPR 2023)

This is the pytorch implementation of the paper (accepted by CVPR 2023).

Fig 1.SANM framework

Training

First you need to install dependencies by running pip install -r requirements.txt.

Then, please create a folder named checkpoint to store the results.

mkdir checkpoint

Next, run

python Train_{dataset_name}.py --data_path <i>path-to-your-data</i>

Performance

Videos

For the introduction of the paper, you can refer to bilibili or youtube for more details.

Citation

If you find SANM useful in your research, please consider citing.

@inproceedings{tu2023learning,
  title={Learning with Noisy labels via Self-supervised Adversarial Noisy Masking},
  author={Tu, Yuanpeng and Zhang, Boshen and Li, Yuxi and Liu, Liang and Li, Jian and Zhang, Jiangning and Wang, Yabiao and Wang, Chengjie and Zhao, Cai Rong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16186--16195},
  year={2023}
}

Reference

For C2D and DivideMix, you can refer to C2D and DivideMix and combine them with our SANM. Thanks for their great work!